Comparison of three longitudinal analysis models for the health-related quality of life in oncology: a simulation study

Health and Quality of Life Outcomes

Caroline Bascoul-Mollevi¹  Franck Bonnetain²  Sophie Gourgou-Bourgade¹  Alhousseiny Pam²  Marion Savina⁴  Antoine Barbieri³  Amélie Anota² 

[1] Biostatistic unit, Institut régional du Cancer de Montpellier (ICM) - Val d’Aurelle, Montpellier, France;Methodological and Quality of Life in Oncology Unit, EA 3181, University Hospital of Besançon, Besançon, France;Institut de Mathématiques et de Modélisation de Montpellier, University of Montpellier 2, Montpellier, France;INSERM CIC-EC7 Axe Cancer, Université de Bordeaux, Bordeaux, France

关键词: Oncology clinical trials;    Health-related quality of life;    Statistical methods;    Longitudinal analysis;   

Others  :  1133902
DOI  :  10.1186/s12955-014-0192-2

 received in 2014-08-28, accepted in 2014-12-12,  发布年份 2014

【 摘 要 】

Background

Health-Related Quality of Life (HRQoL) is an important endpoint in oncology clinical trials aiming to investigate the clinical benefit of new therapeutic strategies for the patient. However, the longitudinal analysis of HRQoL remains complex and unstandardized. There is clearly a need to propose accessible statistical methods and meaningful results for clinicians. The objective of this study was to compare three strategies for longitudinal analyses of HRQoL data in oncology clinical trials through a simulation study.

Methods

The methods proposed were: the score and mixed model (SM); a survival analysis approach based on the time to HRQoL score deterioration (TTD); and the longitudinal partial credit model (LPCM). Simulations compared the methods in terms of type I error and statistical power of the test of an interaction effect between treatment arm and time. Several simulation scenarios were explored based on the EORTC HRQoL questionnaires and varying the number of patients (100, 200 or 300), items (1, 2 or 4) and response categories per item (4 or 7). Five or 10 measurement times were considered, with correlations ranging from low to high between each measure. The impact of informative missing data on these methods was also studied to reflect the reality of most clinical trials.

Results

With complete data, the type I error rate was close to the expected value (5%) for all methods, while the SM method was the most powerful method, followed by LPCM. The power of TTD is low for single-item dimensions, because only four possible values exist for the score. When the number of items increases, the power of the SM approach remained stable, those of the TTD method increases while the power of LPCM remained stable. With 10 measurement times, the LPCM was less efficient. With informative missing data, the statistical power of SM and TTD tended to decrease, while that of LPCM tended to increase.

Conclusions

To conclude, the SM model was the most powerful model, irrespective of the scenario considered, and the presence or not of missing data. The TTD method should be avoided for single-item dimensions of the EORTC questionnaire. While the LPCM model was more adapted to this kind of data, it was less efficient than the SM model. These results warrant validation through comparisons on real data.

【 授权许可】

   
2014 Anota et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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